The Gulf of Mexico Eddy Dataset (GOMED), a census of statistically significant eddy-like events from all available surface drifter data

This dataset uses trajectory data from a large set of drifters to extract and analyze displacement signals associated with coherent eddies in the Gulf of Mexico, using a multivariate wavelet ridge analysis as presented in Lilly and Pérez-Brunius (2021). The data includes eddy displacement signals for all ridges, as well as the time-varying ellipse parameters and estimated ellipse center location. The instantaneous frequency is also included, as is the instantaneous bias estimate derived by Lilly and Olhede (2012). The data are organized as appended trajectory data that can be readily separated through the use of the "ids" field. The ridge length (\(L\)),and ridge-averaged circularity (\(\overline{\xi}\)) are also included, as is measure of statistical significance denoted by (\(\rho\)). The dataset is available for download as a NetCDF file. Lilly, J. M. and P. Pérez-Brunius (2021). Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico. <em>Nonlinear Processes in Geophysics</em>, 28: 181–212. https://doi.org/10.5194/npg-28-181-2021. Lilly, J. M. and Olhede, S. C.: Analysis of modulated multivariate oscillations, IEEE T. Signal Proces., 60, 600–612, 2012. 10.1109/TSP.2011.2173681本数据集依托大规模漂流浮标轨迹数据，采用多元小波脊分析（multivariate wavelet ridge analysis）方法提取并分析墨西哥湾内与相干涡旋相关的位移信号，该方法由Lilly与Pérez-Brunius（2021）提出。 数据集包含所有脊线对应的涡旋位移信号、时变椭圆参数以及估算得到的椭圆中心位置。 数据集同时涵盖瞬时频率（instantaneous frequency），以及由Lilly与Olhede（2012）推导得到的瞬时偏差估算值（instantaneous bias estimate）。 数据以附加轨迹数据的形式组织，可通过"ids"字段轻松完成分离。 此外还提供了脊线长度（(L)）、脊线平均圆度（(overline{xi})）以及一项表征统计显著性的指标( ho)。 本数据集以NetCDF文件格式提供下载。 参考文献： 1. Lilly, J. M. 与 P. Pérez-Brunius (2021). 利用小波脊分析从大规模拉格朗日数据集（Lagrangian datasets）提取统计显著涡旋信号及其在墨西哥湾的应用. 《地球物理学非线性过程》, 28: 181–212. https://doi.org/10.5194/npg-28-181-2021. 2. Lilly, J. M. 与 Olhede, S. C.: 调制多元振荡分析, 《IEEE信号处理汇刊（IEEE Transactions on Signal Processing）》, 60, 600–612, 2012. 10.1109/TSP.2011.2173681